Abstract: A multivariate stochastic simulation application that involves the mapping of a primary variable from a combination for sparse primary data and more densely sampled secondary data. The method is applicable when the relationship between the simulated primary variable and one or more secondary variables is non-linear. The method employs a Bayesian updating rule to build a local posterior distribution for the primary variable at each simulated location. The posterior distribution is the product of a Gaussian kernel function obtained by simple kriging of the primary variable and a secondary probability function obtained directly from a scatter diagram between primary and secondary variables.

Abstract: A discretized lithologic model of the subsurface is defined by a regular array of pixels. Each pixel corresponds to one of a finite number of possible lithoclasses such as sand, shale or dolomite. The lithoclasses are unknown except at a small number of sparsely distributed control pixels associated with borehole locations. Associated with each pixel there is a multivariate record of seismic attributes that may be statistically correlatable with the local lithology. A Monte Carlo method is used to simulate the lithoclass spatial distribution by combining the lithologic data at control pixels with the seismic-attribute data records. Using Indicator Kriging, a prior probability distribution of the lithoclasses is calculated for each pixel from the lithology values at neighboring pixels. The likelihood of each lithoclass is also calculated in each pixel from the corresponding conditional probability distribution of seismic attributes.

Abstract: A Monte Carlo statistical method for combining discrete geological measurements of rock properties with continuous measurements of seismic attributes and for converting those combined measurements into a display of the best estimate of subsurface rock classes.